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Found 6 papers, 0 papers with code
Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy
We compare the aggregated LLM predictions on 31 binary questions to that of a crowd of 925 human forecasters from a three-month forecasting tournament.
AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy
Exploratory analyses showed a pronounced effect in one forecasting item, without which we find that the superforecasting assistant increased accuracy by 43%, compared with 28% for the biased assistant.
Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament
Accurately predicting the future would be an important milestone in the capabilities of artificial intelligence.
Divide-and-Conquer Dynamics in AI-Driven Disempowerment
Myopic members prioritize their future well-being less than their present well-being, and are thus disinclined to solidarily support current victims today at personal cost, even if this is necessary to counter the shared threat of AI-driven disempowerment.
AI Deception: A Survey of Examples, Risks, and Potential Solutions
This paper argues that a range of current AI systems have learned how to deceive humans.
Diminished Diversity-of-Thought in a Standard Large Language Model
In another, we found that most but not all "correct answers" were robust to changing the order of answer choices.
